1. Field of the Invention
The present invention relates to an optimum solution method for obtaining an optimum adjustment result based on an optimum value under a plurality of conditions and a subjective evaluation by an individual, for problems that can not be adjusted based on quantitative evaluation criteria since the evaluation criteria are subjective and unclear, including adjustment of acoustic characteristics, image characteristics and the like which are suited to the preferences of the individual, and more particularly to a hearing aid fitting apparatus utilizing the optimum solution method, and a system optimization adjusting method and the apparatus thereof.
2. Description of the Relevant Art
When acoustic characteristics and image characteristics suited to the preferences of an individual are adjusted, the evaluation criteria for these characteristics are extremely subjective and unclear. Since an inclination of the preferences to each characteristic highly varies with users, there is a problem that the adjusted result cannot be evaluated and expressed quantitatively.
In addition, since there is usually a plurality of parameters for adjusting the acoustic characteristics and the image characteristics to be targeted, and an interaction between these parameter values has a strong influence on the user's subjective evaluation, it is further difficult to determine the optimum adjustment result.
To solve these problems, an optimization adjusting method utilizing an interactive genetic algorithm is proposed, for example, in Japanese Unexamined Patent Publication No. Hei 9-54765. According to this method, an n-dimensional vector of which the element is n-units of adjustment parameters is a solution vector (a chromosome), wherein an acoustic signal or a picture signal that is processed according to each solution vector is presented to the user. The genetic algorithm is then performed based on the evaluation value assigned by the user to each solution vector to estimate an optimum solution vector.
According to this method, a characteristic that the user himself subjectively feels to be most comfortable can be computed, not by separately computing the optimum value for each adjustment value, but by taking the interaction between each adjustment value into consideration.
In a conventional interactive genetic algorithm, a method called the elite strategy is often used. In the genetic algorithm, children (solution vectors of the next generation) who are born by crossing their parents (solution vectors) whose evaluation values have been high do not always have evaluation values as high as their parents. There is a problem that the parents who have existed in the preceding generation have higher evaluation values than their children, but the solution vector of the parents can not be reproduced in the following generation and it is also difficult to converge on an optimum solution.
The elite strategy is a method, to avoid such a phenomenon, that leaves an a-units of parents with higher evaluation values to the next generation as is.
Also, another method for determining an optimum image on a certain problem is suggested (SIGGRAPH Conf. Proc., Vol. 1997, pp 389–400, 1997). This is a system that forms an n-dimensional solution vector (n>2) of which the component is a characteristic adjustment value of an image to be targeted. Each solution vector is mapped onto a two-dimensional space for illustration to the user. When the user designates any coordinate within the two-dimensional space, an image, of which the adjustment value is a solution vector corresponding to the coordinate, is presented to that user. According to this method, each solution vector is mapped onto the two-dimensional space utilizing MDS (Multidimensional Scaling) and the like based, on a Euclidean distance between each vector, and an optimum value can be determined, while allowing the user to image the distance in the multidimensional space, in the two-dimensional space.
A hearing aid fitting operation is considered to be one example of problems that determine the acoustic characteristic, the image characteristic, and the like that are suited to the preferences of an individual, which is a subject of the present invention. Hearing characteristics of a hearing impaired person vary with individuals and their preferences for a sound also differ. Most hearing aids are provided with a plurality of adjustment functions (for example, volume control, frequency response control, output limit control, automatic gain control, etc.) to suit different types of hearing impaired persons.
Hearing aid fittings are operations for setting the degree of adjustment (adjustment value) for each adjustment function at a value optimum for each hearing impaired person. The fitting operation is usually conducted by substituting a value of an audiogram and the like in a known fitting formula. On the other hand, Japanese Unexamined Patent Publication No. Hei 9-54765 proposes a method for performing the hearing aid fitting operation using the interactive genetic algorithm in which the n-dimensional solution vector is composed by using the adjustment value of each adjustment function.
However, in the interactive genetic algorithm, there is a problem that a single optimum value is determined on a single condition for a certain problem and as a result, the optimum value specific for that condition, i.e. for the condition used in the adjustment, has been determined. Accordingly, in a problem in which there is a plurality of conditions, the interactive genetic algorithm must be conducted on each condition and the optimum value specific for each condition must be determined, wherein the final single optimum value must be separately determined. This final optimum value has been determined by the operator's subjective evaluation, or the formula and the like, that are prepared irrespective of each user's preferences.
For example, in the hearing aid fitting operation, when any single sound source (for example, a speech signal) is used for performing the interactive genetic algorithm, there is a problem that the optimum value specific for that sound source has been determined.
The hearing aid is an apparatus that is used under various environments. The hearing impaired persons must be provided with comfortable hearing conditions under any environments. Accordingly, it is necessary to perform the interactive genetic algorithm on a plurality of conditions (for example, a plurality of environmental sounds), not on a single sound source, in which an optimum value must be collected from each operation of the genetic algorithm before determining the final optimum value.
However, there is still a problem that this final optimum value must be determined by the operator's subjective evaluation, or the formula and the like, that are prepared irrespective of each user's preferences.
In the method in which the multidimensional solution vector is mapped onto the two-dimensional space so that the user can determine the optimum value, if the dimension number of the solution vector and/or the number of bits of the components (a gene) of the solution vector are large, the number of optimum solution vector candidates to be illustrated in the two-dimensional space becomes large. Thus, it takes a long time to determine the optimum value and there is a problem that a burden imposed on the user also increases.
For example, in the hearing aid fitting operation, when the multidimensional solution vector is mapped onto the two-dimensional space so that the user can determine the optimum value, the number of optimum solution vector candidates illustrated to the hearing impaired person becomes enormous, depending upon the number of adjustment functions of the hearing aid and/or the number of bits of the adjustment value of each adjustment function. Thus, there is a problem that the time required for fitting is very long and the burden imposed on the hearing-impaired person also increases.
In the interactive genetic algorithm, there is a problem that it is difficult for the user to judge the criteria for the evaluation value. The judgment criteria of a human being are vague, and when the solution vector that has received a higher evaluation is reproduced in the next generation, the user does not always evaluate it higher.
Many users cannot remember acoustic characteristics of the solution vector generated until then. Even though the same or extremely similar solution vectors are reproduced in the next generation, it is difficult for the user to realize that these are the vectors that have appeared before and as a result, there is a problem that the user has evaluated differently from the last time. This indicates that the user's evaluation criteria change whenever the generation of the genetic algorithm is altered.
In the interactive genetic algorithm, the optimum value is sought based on the user's evaluation. Fluctuations in such an evaluation exert a great influence on convergent speed and accuracy of the optimum value.
Even though the elite strategy is employed, it is very difficult to identify the elite in the preceding generation from among a plurality of solution vectors in the new generation. It has been impossible to reduce these fluctuations in evaluation.
For example, in the case of the hearing aid fitting operation, when the solution vector (fitting value) on which the hearing impaired user has set a high evaluation is presented to him again, he does not always set a higher evaluation on it. Accordingly, there is still a problem that the user sets a different evaluation value on the same vector than before whenever the generation of the genetic algorithm is altered.
Even though the elite strategy is applied, it is very hard for the user to locate the elite. Therefore, there is a problem that the elite does not serve as judgment criteria and the judgment criteria have also changed when the generation is altered.